{"product_id":"introductory-statistics-and-analytics-9781118881354","title":"Introductory Statistics and Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eConcise, thoroughly class-tested primer that features basic statistical concepts in the concepts in the context of analytics, resampling, and the bootstrap\u003cbr\u003e \u003cbr\u003e \u003c\/b\u003eA uniquely developed presentation of key statistical topics, \u003ci\u003eIntroductory Statistics and Analytics: A Resampling Perspective\u003c\/i\u003e provides an accessible approach to statistical analytics, resampling, and the bootstrap for readers with various levels of exposure to basic probability and statistics. Originally class-tested at one of the first online learning companies in the discipline, www.statistics.com, the book primarily focuses on applications of statistical concepts developed via resampling, with a background discussion of mathematical theory. This feature stresses statistical literacy and understanding, which demonstrates the fundamental basis for statistical inference and demystifies traditional formulas.\u003cbr\u003e \u003cbr\u003e The book begins with illustrations that have the essential statistical topics interwo\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrade Review\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e“The book is an excellent primary textbook for courses in preliminary statistics as well as a supplement for courses in upper-level statistics and related fields, such as biostatistics and econometrics. The book is also a general reference for readers interested in revisiting the value of statistics.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 April 2015)\u003c\/p\u003e \u003cp\u003e \u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface ix\u003c\/p\u003e \u003cp\u003eAcknowledgments xi\u003c\/p\u003e \u003cp\u003eIntroduction xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Designing and Carrying Out a Statistical Study 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 A Small Example 3\u003c\/p\u003e \u003cp\u003e1.2 Is Chance Responsible? The Foundation of Hypothesis Testing 3\u003c\/p\u003e \u003cp\u003e1.3 A Major Example 7\u003c\/p\u003e \u003cp\u003e1.4 Designing an Experiment 8\u003c\/p\u003e \u003cp\u003e1.5 What to Measure—Central Location 13\u003c\/p\u003e \u003cp\u003e1.6 What to Measure—Variability 16\u003c\/p\u003e \u003cp\u003e1.7 What to Measure—Distance (Nearness) 19\u003c\/p\u003e \u003cp\u003e1.8 Test Statistic 21\u003c\/p\u003e \u003cp\u003e1.9 The Data 22\u003c\/p\u003e \u003cp\u003e1.10 Variables and Their Flavors 28\u003c\/p\u003e \u003cp\u003e1.11 Examining and Displaying the Data 31\u003c\/p\u003e \u003cp\u003e1.12 Are we Sure we Made a Difference? 39\u003c\/p\u003e \u003cp\u003eAppendix: Historical Note 39\u003c\/p\u003e \u003cp\u003e1.13 Exercises 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Statistical Inference 45\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Repeating the Experiment 46\u003c\/p\u003e \u003cp\u003e2.2 How Many Reshuffles? 48\u003c\/p\u003e \u003cp\u003e2.3 How Odd is Odd? 53\u003c\/p\u003e \u003cp\u003e2.4 Statistical and Practical Significance 55\u003c\/p\u003e \u003cp\u003e2.5 When to use Hypothesis Tests 56\u003c\/p\u003e \u003cp\u003e2.6 Exercises 56\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Displaying and Exploring Data 59\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Bar Charts 59\u003c\/p\u003e \u003cp\u003e3.2 Pie Charts 61\u003c\/p\u003e \u003cp\u003e3.3 Misuse of Graphs 62\u003c\/p\u003e \u003cp\u003e3.4 Indexing 64\u003c\/p\u003e \u003cp\u003e3.5 Exercises 68\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Probability 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Mendel’s Peas 72\u003c\/p\u003e \u003cp\u003e4.2 Simple Probability 73\u003c\/p\u003e \u003cp\u003e4.3 Random Variables and their Probability Distributions 77\u003c\/p\u003e \u003cp\u003e4.4 The Normal Distribution 80\u003c\/p\u003e \u003cp\u003e4.5 Exercises 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Relationship between Two Categorical Variables 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Two-Way Tables 87\u003c\/p\u003e \u003cp\u003e5.2 Comparing Proportions 90\u003c\/p\u003e \u003cp\u003e5.3 More Probability 92\u003c\/p\u003e \u003cp\u003e5.4 From Conditional Probabilities to Bayesian Estimates 95\u003c\/p\u003e \u003cp\u003e5.5 Independence 97\u003c\/p\u003e \u003cp\u003e5.6 Exploratory Data Analysis (EDA) 99\u003c\/p\u003e \u003cp\u003e5.7 Exercises 100\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Surveys and Sampling 104\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Simple Random Samples 105\u003c\/p\u003e \u003cp\u003e6.2 Margin of Error: Sampling Distribution for a Proportion 109\u003c\/p\u003e \u003cp\u003e6.3 Sampling Distribution for a Mean 111\u003c\/p\u003e \u003cp\u003e6.4 A Shortcut—the Bootstrap 113\u003c\/p\u003e \u003cp\u003e6.5 Beyond Simple Random Sampling 117\u003c\/p\u003e \u003cp\u003e6.6 Absolute Versus Relative Sample Size 120\u003c\/p\u003e \u003cp\u003e6.7 Exercises 120\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Confidence Intervals 124\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Point Estimates 124\u003c\/p\u003e \u003cp\u003e7.2 Interval Estimates (Confidence Intervals) 125\u003c\/p\u003e \u003cp\u003e7.3 Confidence Interval for a Mean 126\u003c\/p\u003e \u003cp\u003e7.4 Formula-Based Counterparts to the Bootstrap 126\u003c\/p\u003e \u003cp\u003e7.5 Standard Error 132\u003c\/p\u003e \u003cp\u003e7.6 Confidence Intervals for a Single Proportion 133\u003c\/p\u003e \u003cp\u003e7.7 Confidence Interval for a Difference in Means 136\u003c\/p\u003e \u003cp\u003e7.8 Confidence Interval for a Difference in Proportions 139\u003c\/p\u003e \u003cp\u003e7.9 Recapping 140\u003c\/p\u003e \u003cp\u003eAppendix A: More on the Bootstrap 141\u003c\/p\u003e \u003cp\u003eResampling Procedure—Parametric Bootstrap 141\u003c\/p\u003e \u003cp\u003eFormulas and the Parametric Bootstrap 144\u003c\/p\u003e \u003cp\u003eAppendix B: Alternative Populations 144\u003c\/p\u003e \u003cp\u003eAppendix C: Binomial Formula Procedure 144\u003c\/p\u003e \u003cp\u003e7.10 Exercises 147\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Hypothesis Tests 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 Review of Terminology 151\u003c\/p\u003e \u003cp\u003e8.2 A–B Tests: The Two Sample Comparison 154\u003c\/p\u003e \u003cp\u003e8.3 Comparing Two Means 156\u003c\/p\u003e \u003cp\u003e8.4 Comparing Two Proportions 157\u003c\/p\u003e \u003cp\u003e8.5 Formula-Based Alternative—\u003ci\u003et\u003c\/i\u003e-Test for Means 159\u003c\/p\u003e \u003cp\u003e8.6 The Null and Alternative Hypotheses 160\u003c\/p\u003e \u003cp\u003e8.7 Paired Comparisons 163\u003c\/p\u003e \u003cp\u003eAppendix A: Confidence Intervals Versus Hypothesis Tests 167\u003c\/p\u003e \u003cp\u003eConfidence Interval 168\u003c\/p\u003e \u003cp\u003eRelationship Between the Hypothesis Test and the Confidence Interval 169\u003c\/p\u003e \u003cp\u003eComment 170\u003c\/p\u003e \u003cp\u003eAppendix B: Formula-Based Variations of Two-Sample Tests 170\u003c\/p\u003e \u003cp\u003e\u003ci\u003eZ\u003c\/i\u003e-Test With Known Population Variance 170\u003c\/p\u003e \u003cp\u003ePooled Versus Separate Variances 171\u003c\/p\u003e \u003cp\u003eFormula-Based Alternative: \u003ci\u003eZ\u003c\/i\u003e-Test for Proportions 172\u003c\/p\u003e \u003cp\u003e8.8 Exercises 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Hypothesis Testing—2 178\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 A Single Proportion 178\u003c\/p\u003e \u003cp\u003e9.2 A Single Mean 180\u003c\/p\u003e \u003cp\u003e9.3 More Than Two Categories or Samples 181\u003c\/p\u003e \u003cp\u003e9.4 Continuous Data 187\u003c\/p\u003e \u003cp\u003e9.5 Goodness-of-Fit 187\u003c\/p\u003e \u003cp\u003eAppendix: Normal Approximation; Hypothesis Test of a Single Proportion 190\u003c\/p\u003e \u003cp\u003eConfidence Interval for a Mean 190\u003c\/p\u003e \u003cp\u003e9.6 Exercises 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Correlation 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Example: Delta Wire 194\u003c\/p\u003e \u003cp\u003e10.2 Example: Cotton Dust and Lung Disease 195\u003c\/p\u003e \u003cp\u003e10.3 The Vector Product and Sum Test 196\u003c\/p\u003e \u003cp\u003e10.4 Correlation Coefficient 199\u003c\/p\u003e \u003cp\u003e10.5 Other Forms of Association 204\u003c\/p\u003e \u003cp\u003e10.6 Correlation is not Causation 205\u003c\/p\u003e \u003cp\u003e10.7 Exercises 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Regression 209\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Finding the Regression Line by Eye 210\u003c\/p\u003e \u003cp\u003e11.2 Finding the Regression Line by Minimizing Residuals 212\u003c\/p\u003e \u003cp\u003e11.3 Linear Relationships 213\u003c\/p\u003e \u003cp\u003e11.4 Inference for Regression 217\u003c\/p\u003e \u003cp\u003e11.5 Exercises 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Analysis of Variance—ANOVA 224\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1 Comparing More Than Two Groups: ANOVA 225\u003c\/p\u003e \u003cp\u003e12.2 The Problem of Multiple Inference 228\u003c\/p\u003e \u003cp\u003e12.3 A Single Test 229\u003c\/p\u003e \u003cp\u003e12.4 Components of Variance 230\u003c\/p\u003e \u003cp\u003e12.5 Two-Way ANOVA 240\u003c\/p\u003e \u003cp\u003e12.6 Factorial Design 246\u003c\/p\u003e \u003cp\u003e12.7 Exercises 248\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Multiple Regression 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1 Regression as Explanation 252\u003c\/p\u003e \u003cp\u003e13.2 Simple Linear Regression—Explore the Data First 253\u003c\/p\u003e \u003cp\u003e13.3 More Independent Variables 257\u003c\/p\u003e \u003cp\u003e13.4 Model Assessment and Inference 261\u003c\/p\u003e \u003cp\u003e13.5 Assumptions 267\u003c\/p\u003e \u003cp\u003e13.6 Interaction Again 270\u003c\/p\u003e \u003cp\u003e13.7 Regression for Prediction 272\u003c\/p\u003e \u003cp\u003e13.8 Exercises 277\u003c\/p\u003e \u003cp\u003eIndex 283\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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